The relation of alexithymia and attachment with type 1 diabetes management in adolescents: a gender-specific analysis
Bibliographic record
Abstract
BACKGROUND: Several studies indicate the role of psychosocial factors in the management and control of chronic diseases in adolescents. In this regard, the roles of attachment and alexithymia in the management of type 1 diabetes in adolescents and related gender-specific patterns have rarely been the focus of empirical research. In this study we investigate the gender-specific relationship of alexithymia and attachment with self-care and blood glucose level in adolescents with type1 diabetes. METHODS: This is a cross-sectional study conducted on adolescents aged 12-18 years, with type 1 diabetes. Participants were recruited from diabetes clinics and the Iranian Diabetes Society. Data were collected using the Farsi versions of the Toronto Alexithymia Scale (FTAS-20), the Inventory of Parent and Peer Attachment (IPPA) and the Summary of Diabetes Self-Care Activities Scale (SDSCA). Blood glucose levels were measured by determining HbA1c which were abstracted from medical records. Data were analyzed using SPSS21 software. RESULTS: Participants were 150 adolescents (57% female), mean age 14.97 ± 2.30. Alexithymia (β = 0.10, P = 0.01), difficulty identifying feelings (β = 0.15, P = 0.03) and communication with mothers (β = - 0.08, P = 0.03) predicted HbA1c in girls, whereas no significant relationships were observed for HbA1c with alexithymia and attachment in boys. Factors that predicted self-care in girls were alexithymia (β = - 0.04, P = 0.02), difficulty identifying feelings (β = - 0.06, P = 0.04); in boys however in addition to these two factors predicting self-care [alexithymia (β = - 0.07, P = 0.01) and difficulty identifying feelings (β = - 0.11, P = 0.01)], we also found difficulty describing feelings (β = - 0.16, P = 0.02), communication with mother (β = 0.04, P = 0.04), alienation to mother (β = - 0.06, P = 0.03), to father (β = - 0.06, P = 0.01) and to peers (β = - 0.09, P = 0.03). CONCLUSIONS: Our results suggest that, in a gender-specific pattern, alexithymia and attachment could affect self-care and blood glucose level in adolescents with type 1 diabetes; findings that can be used to facilitate more effective treatment strategies and interventions in this age group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".